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624
Similarity search in high dimensions via hashing
, 1999
"... The nearest or nearneighbor query problems arise in a large variety of database applications, usually in the context of similarity searching. Of late, there has been increasing interest in building search/index structures for performing similarity search over highdimensional data, e.g., image dat ..."
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Cited by 622 (13 self)
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The nearest or nearneighbor query problems arise in a large variety of database applications, usually in the context of similarity searching. Of late, there has been increasing interest in building search/index structures for performing similarity search over highdimensional data, e.g., image databases, document collections, timeseries databases, and genome databases. Unfortunately, all known techniques for solving this problem fall prey to the \curse of dimensionality. &quot; That is, the data structures scale poorly with data dimensionality; in fact, if the number of dimensions exceeds 10 to 20, searching in kd trees and related structures involves the inspection of a large fraction of the database, thereby doing no better than bruteforce linear search. It has been suggested that since the selection of features and the choice of a distance metric in typical applications is rather heuristic, determining an approximate nearest neighbor should su ce for most practical purposes. In this paper, we examine a novel scheme for approximate similarity search based on hashing. The basic idea is to hash the points
Discovering similar multidimensional trajectories
 In ICDE
, 2002
"... We investigate techniques for analysis and retrieval of object trajectories in a two or three dimensional space. Such kind of data usually contain a great amount of noise, that makes all previously used metrics fail. Therefore, here we formalize nonmetric similarity functions based on the Longest C ..."
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Cited by 253 (6 self)
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We investigate techniques for analysis and retrieval of object trajectories in a two or three dimensional space. Such kind of data usually contain a great amount of noise, that makes all previously used metrics fail. Therefore, here we formalize nonmetric similarity functions based on the Longest Common Subsequence (LCSS), which are very robust to noise and furthermore provide an intuitive notion of similarity between trajectories by giving more weight to the similar portions of the sequences. Stretching of sequences in time is allowed, as well as global translating of the sequences in space. Efficient approximate algorithms that compute these similarity measures are also provided. We compare these new methods to the widely used Euclidean and Time Warping distance functions (for real and synthetic data) and show the superiority of our approach, especially under the strong presence of noise. We prove a weaker version of the triangle inequality and employ it in an indexing structure to answer nearest neighbor queries. Finally, we present experimental results that validate the accuracy and efficiency of our approach. 1
PeertoPeer Information Retrieval Using SelfOrganizing Semantic Overlay Networks
, 2003
"... Contentbased fulltext search is a challenging problem in PeertoPeer (P2P) systems. Traditional approaches have either been centralized or use flooding to ensure accuracy of the results returned. In this paper, we present pSearch, a decentralized nonflooding P2P information retrieval system. pSea ..."
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Cited by 235 (7 self)
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Contentbased fulltext search is a challenging problem in PeertoPeer (P2P) systems. Traditional approaches have either been centralized or use flooding to ensure accuracy of the results returned. In this paper, we present pSearch, a decentralized nonflooding P2P information retrieval system. pSearch distributes document indices through the P2P network based on document semantics generated by Latent Semantic Indexing (LSI). The search cost (in terms of different nodes searched and data transmitted) for a given query is thereby reduced, since the indices of semantically related documents are likely to be colocated in the network. We also describe techniques that help distribute the indices more evenly across the nodes, and further reduce the number of nodes accessed using appropriate index distribution as well as using index samples and recently processed queries to guide the search. Experiments show that pSearch can achieve performance comparable to centralized information retrieval systems by searching only a small number of nodes. For a system with 128,000 nodes and 528,543 documents (from news, magazines, etc.), pSearch searches only 19 nodes and transmits only 95.5KB data during the search, whereas the top 15 documents returned by pSearch and LSI have a 91.7% intersection.
Product quantization for nearest neighbor search
, 2010
"... This paper introduces a product quantization based approach for approximate nearest neighbor search. The idea is to decomposes the space into a Cartesian product of low dimensional subspaces and to quantize each subspace separately. A vector is represented by a short code composed of its subspace q ..."
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Cited by 221 (31 self)
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This paper introduces a product quantization based approach for approximate nearest neighbor search. The idea is to decomposes the space into a Cartesian product of low dimensional subspaces and to quantize each subspace separately. A vector is represented by a short code composed of its subspace quantization indices. The Euclidean distance between two vectors can be efficiently estimated from their codes. An asymmetric version increases precision, as it computes the approximate distance between a vector and a code. Experimental results show that our approach searches for nearest neighbors efficiently, in particular in combination with an inverted file system. Results for SIFT and GIST image descriptors show excellent search accuracy outperforming three stateoftheart approaches. The scalability of our approach is validated on a dataset of two billion vectors.
3D shape histograms for similarity search and classification in spatial databases
 SSD'99
, 1999
"... Classification is one of the basic tasks of data mining in modern database applications including molecular biology, astronomy, mechanical engineering, medical imaging or meteorology. The underlying models have to consider spatial properties such as shape or extension as well as thematic attributes ..."
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Cited by 176 (11 self)
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Classification is one of the basic tasks of data mining in modern database applications including molecular biology, astronomy, mechanical engineering, medical imaging or meteorology. The underlying models have to consider spatial properties such as shape or extension as well as thematic attributes. We introduce 3D shape histograms as an intuitive and powerful similarity model for 3D objects. Particular flexibility is provided by using quadratic form distance functions in order to account for errors of measurement, sampling, and numerical rounding that all may result in small displacements and rotations of shapes. For query processing, a general filterrefinement architecture is employed that efficiently supports similarity search based on quadratic forms. An experimental evaluation in the context of molecular biology demonstrates both, the high classification accuracy of more than 90 % and the good performance of the approach.
Continuous Nearest Neighbor Search
, 2002
"... A continuous nearest neighbor query retrieves the nearest neighbor (NN) of every point on a line segment (e.g., "find all my nearest gas stations during my route from point s to point e"). The result contains a set of <point, interval> tuples, such that point is the NN of all po ..."
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Cited by 159 (10 self)
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A continuous nearest neighbor query retrieves the nearest neighbor (NN) of every point on a line segment (e.g., "find all my nearest gas stations during my route from point s to point e"). The result contains a set of <point, interval> tuples, such that point is the NN of all points in the corresponding interval. Existing methods for continuous nearest neighbor search are based on the repetitive application of simple NN algorithms, which incurs significant overhead. In this paper we propose techniques that solve the problem by performing a single query for the whole input segment. As a result the cost, depending on the query and dataset characteristics, may drop by orders of magnitude.
Robust and fast similarity search for moving object trajectories
 In Proc. ACM SIGMOD Int. Conf. on Management of Data
, 2005
"... An important consideration in similaritybased retrieval of moving object trajectories is the definition of a distance function. The existing distance functions are usually sensitive to noise, shifts and scaling of data that commonly occur due to sensor failures, errors in detection techniques, dis ..."
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Cited by 150 (14 self)
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An important consideration in similaritybased retrieval of moving object trajectories is the definition of a distance function. The existing distance functions are usually sensitive to noise, shifts and scaling of data that commonly occur due to sensor failures, errors in detection techniques, disturbance signals, and different sampling rates. Cleaning data to eliminate these is not always possible. In this paper, we introduce a novel distance function, Edit Distance on Real sequence (EDR) which is robust against these data imperfections. Analysis and comparison of EDR with other popular distance functions, such as Euclidean distance, Dynamic Time Warping (DTW), Edit distance with Real Penalty (ERP), and Longest Common Subsequences (LCSS), indicate that EDR is more robust than Euclidean distance, DTW and ERP, and it is on average 50% more accurate than LCSS. We also develop three pruning techniques to improve the retrieval efficiency of EDR and show that these techniques can be combined effectively in a search, increasing the pruning power significantly. The experimental results confirm the superior efficiency of the combined methods. 1.
A survey of contentbased image retrieval with highlevel semantics
, 2007
"... In order to improve the retrieval accuracy of contentbased image retrieval systems, research focus has been shifted from designing sophisticated lowlevel feature extraction algorithms to reducing the ‘semantic gap ’ between the visual features and the richness of human semantics. This paper attemp ..."
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Cited by 142 (5 self)
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In order to improve the retrieval accuracy of contentbased image retrieval systems, research focus has been shifted from designing sophisticated lowlevel feature extraction algorithms to reducing the ‘semantic gap ’ between the visual features and the richness of human semantics. This paper attempts to provide a comprehensive survey of the recent technical achievements in highlevel semanticbased image retrieval. Major recent publications are included in this survey covering different aspects of the research in this area, including lowlevel image feature extraction, similarity measurement, and deriving highlevel semantic features. We identify five major categories of the stateoftheart techniques in narrowing down the ‘semantic gap’: (1) using object ontology to define highlevel concepts; (2) using machine learning methods to associate lowlevel features with query concepts; (3) using relevance feedback to learn users’ intention; (4) generating semantic template to support highlevel image retrieval; (5) fusing the evidences from HTML text and the visual content of images for WWW image retrieval. In addition, some other related issues such as image test bed and retrieval performance evaluation are also discussed. Finally, based on existing technology and the demand from realworld applications, a few promising future research directions are suggested.
What is the Nearest Neighbor in High Dimensional Spaces?
, 2000
"... Nearest neighbor search in high dimensional spaces is an interesting and important problem which is relevant for a wide variety of novel database applications. As recent results show, however, the problem is a very difficult one, not only with regards to the performance issue but also to the quality ..."
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Cited by 135 (10 self)
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Nearest neighbor search in high dimensional spaces is an interesting and important problem which is relevant for a wide variety of novel database applications. As recent results show, however, the problem is a very difficult one, not only with regards to the performance issue but also to the quality issue. In this paper, we discuss the quality issue and identify a new generalized notion of nearest neighbor search as the relevant problem in high dimensional space. In contrast to previous approaches, our new notion of nearest neighbor search does not treat all dimensions equally but uses a quality criterion to select relevant dimensions (projections) with respect to the given query. As an example for a useful quality criterion, we rate how well the data is clustered around the query point within the selected projection. We then propose an efficient and effective algorithm to solve the generalized nearest neighbor problem. Our experiments based on a number of real and synthetic data sets show that our new approach provides new insights into the nature of nearest neighbor search on high dimensional data.
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
, 2000
"... Many emerging application domains require database systems to support efficient access over highly multidimensional datasets. The current stateoftheart technique to indexing high dimensional data is to first reduce the dimensionality of the data using Principal Component Analysis and then in ..."
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Cited by 120 (3 self)
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Many emerging application domains require database systems to support efficient access over highly multidimensional datasets. The current stateoftheart technique to indexing high dimensional data is to first reduce the dimensionality of the data using Principal Component Analysis and then indexing the reduced dimensionality space using a multidimensional index structure. The above technique, referred to as global dimensionality reduction (GDR), works well when the data set is globally correlated, i.e. most of the variation in the data can be captured by a few dimensions. In practice, datasets are often not globally correlated. In such cases, reducing the data dimensionality using GDR causes significant loss of distance information resulting in a large number of false positives and hence a high query cost. Even when a global correlation does not exist, there may exist subsets of data that are locally correlated. In this paper, we propose a technique called Local Dime...